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Simultaneous reconstruction and denoising of seismic data based on rank reduction and sparsity constraints |
LI Wen-Jie1( ), ZHANG Hua1( ), REN Wang1, YE Hai-Long2, WU Zhao-Qi1, YANG Xi-Xi1, PENG Qing1 |
1. State Key Laboratory of Nuclear Resources and Environment, East China University of Technology, Nanchang 330013,China 2. Hydrogeological Brigade of Jiangxi Bureau of Geology, Nanchang 330013,China |
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Abstract Field seismic data contain various random noise and irregular channel missing. Their simultaneous reconstruction and denoising is necessary for subsequent data processing. Currently, most simultaneous reconstruction and denoising methods only use a single sparsity or rank reduction constraint. The sparsity constraint exhibits high efficiency but lacks adaptability to various data. In contrast, the rank reduction constraint can adapt to various data but shows a high computational cost. To take a full advantage of different constraints, this study proposed a method for simultaneous reconstruction and denoising of seismic data based on combined constraints. This method regards projection onto convex sets (POCS) based on Fourier transform as the sparsity constraint, and damped multichannel singular spectrum analysis (DMSSA) as the rank reduction constraint. It employs the truncated singular value decomposition (TSVD) algorithm and the exponential threshold equation, fully utilizing the high computational efficiency of the sparsity constraint and the strong adaptability of the rank reduction constraint. As indicated by the processing results of theoretical and field data, this method based on combined constraints can consider and utilize the spatio-temporal correlations of seismic data, achieving higher signal-to-noise ratios via fewer iterations compared to methods based on a single constraint.
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Received: 24 September 2023
Published: 16 April 2024
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Corresponding Authors:
ZHANG Hua
E-mail: 471202090@qq.com;zhhua1979@163.com
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Reconstruction denoising flowchart under joint constraint conditions
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Linear data slicing
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Comparison of slices for reconstructing denoising results using different methods on linear data
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Comparison of signal-to-noise ratios under different conditions
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Simultaneous reconstruction and denoising results of linear 3D data
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Nonlinear 3D data and its simultaneous reconstruction and denoising results
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Slice comparison of processing results for nonlinear data
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Complex nonlinear 3D data and its simultaneous reconstruction and denoising results
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Comparison of processing results of measured data
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Amplitude spectrum comparison of a single trace
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